CMA-ES with Surrogate Model Adapting to Fitness Landscape

被引：1

作者：

Tsukada, Kento ^{[1
]}

Hasegawa, Taku ^{[1
]}

Mori, Naoki ^{[1
]}

Matsumoto, Keinosuke ^{[1
]}

机构：

[1] Osaka Prefecture Univ, Grad Sch Engn, 1-1 Gakuencho, Sakai, Osaka 5998531, Japan

来源：

INTELLIGENT AND EVOLUTIONARY SYSTEMS, IES 2016 | 2017年 / 8卷

关键词：

Evolutionary computation; Support vector machine; Continuous optimization;

D O I：

10.1007/978-3-319-49049-6_30

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

One of the most important issues for evolutionary computation (EC) is to consider the number of fitness evaluations. In order to reduce the number of fitness evaluations, we have proposed the novel surrogate model called Rank Space Estimation (RSE) model and the surrogate-assisted EC with RSE model called the Fitness Landscape Learning Evolutionary Computation (FLLEC). This paper presents a novel CMA-ES with RSE model for continuous optimization problems and a scaling method for input data to surrogate model.

引用

页码：417 / 429

页数：13

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